On 2013 Oct 23, Stephen Turner commented:

This study set out to compare shotgun metagenomic sequencing to 16S rRNA amplicon sequencing to determine the taxonomic and abundance profiles of mixed community metagenomic samples. Thus far, benchmarking metagenomic methodology has been difficult due to the lack of datasets where the underlying ground truth is known. In this study, the researchers constructed synthetic metagenomic communities consisting of 64 laboratory mixed genome DNAs of known sequence and polymerase chain reaction (PCR)-validated abundance. The researchers then compared metagenomic and 16S amplicon sequencing, using both 454 and Illumina technology, and found that metagenomic sequencing outperformed 16S sequencing in quantifying community composition. The synthetic metagenomes constructed here are publicly available (Gene Expression Omnibus [GEO] accession numbers are given in the manuscript), which represent a great asset to other researchers developing methods for amplicon-based or metagenomic approaches to sequence classification, diversity analysis, and abundance estimation.

On 2013 Oct 23, Stephen Turner commented:

This study set out to compare shotgun metagenomic sequencing to 16S rRNA amplicon sequencing to determine the taxonomic and abundance profiles of mixed community metagenomic samples. Thus far, benchmarking metagenomic methodology has been difficult due to the lack of datasets where the underlying ground truth is known. In this study, the researchers constructed synthetic metagenomic communities consisting of 64 laboratory mixed genome DNAs of known sequence and polymerase chain reaction (PCR)-validated abundance. The researchers then compared metagenomic and 16S amplicon sequencing, using both 454 and Illumina technology, and found that metagenomic sequencing outperformed 16S sequencing in quantifying community composition. The synthetic metagenomes constructed here are publicly available (Gene Expression Omnibus [GEO] accession numbers are given in the manuscript), which represent a great asset to other researchers developing methods for amplicon-based or metagenomic approaches to sequence classification, diversity analysis, and abundance estimation.